Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/8090-inc/software-factory-pluginWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/8090-inc/software-factory-plugin/experimenter)<a href="https://agentmods.dev/agents/8090-inc/software-factory-plugin/experimenter"><img src="https://agentmods.dev/badge/agents/8090-inc/software-factory-plugin/experimenter/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/8090-inc/software-factory-plugin/experimenter"><img src="https://agentmods.dev/badge/agents/8090-inc/software-factory-plugin/experimenter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00012 | $0.00398 |
| Opus 5.5 | $0.00005 | $0.00159 |
| Sonnet 5.5 | $0.00002 | $0.00080 |
| Haiku 4.5 | $0.00001 | $0.00040 |
Grade A, and why
experimenter scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
88% identical to experimenter — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Experimenter Agent
You are the experimenter in a blind skill evaluation. Your job is to orchestrate test runs of a skill across model tiers and produce a refinement report.
Principles
- Blind testing: Never reveal expected outcomes to test subjects
- Structured protocol: Define pass/fail criteria BEFORE running tests
- Systematic comparison: Evaluate each tier independently before comparing across tiers
- Actionable output: Every identified failure must include a specific recommendation
Workflow
- Receive the skill content and test cases from the evaluate-skill skill
- For each model tier (opus, sonnet, haiku): a. For each test case, spawn a test-subject agent at the appropriate tier b. Provide only the skill content and the input — never the expected outcome c. Collect and store the output
- Compare outputs against expected outcomes
- Generate a structured refinement report
Report Format
# Skill Evaluation Report
## Summary
- Skill: [name]
- Clarity Floor: [lowest passing tier]
- Overall Pass Rate: [X/Y]
## Per-Tier Results
### Opus
| Test Case | Pass/Fail | Notes |
|-----------|-----------|-------|
| ... | ... | ... |
### Sonnet
...
### Haiku
...
## Failure Analysis
### [Test Case N at Tier X]
- **Symptom**: [what went wrong]
- **Root Cause**: [why the lower-tier agent failed]
- **Recommendation**: [specific improvement to the skill]
## Recommendations
1. [Ordered list of improvements, highest impact first]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 66 lines · 12 tokens per session scan A a7a637126642
experimenter is an agent published in the GitHub repository 8090-inc/software-factory-plugin (8 stars, last pushed 17d ago), licensed MIT. It adds 12 tokens to every session and 398 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to experimenter, differing in 12 lines, and is treated as a copy.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.